Sparse Predictive Structure of Deconvolved Functional Brain Networks

The functional and structural representation of the brain as a complex network is marked by the fact that the comparison of noisy and intrinsically correlated high-dimensional structures between experimental conditions or groups shuns typical mass univariate methods. Furthermore most network estimation methods cannot distinguish between real and spurious correlation arising from the convolution due to nodes' interaction, which thus introduces additional noise in the data. We propose a machine learning pipeline aimed at identifying multivariate differences between brain networks associated to different experimental conditions. The pipeline (1) leverages the deconvolved individual contribution of each edge and (2) maps the task into a sparse classification problem in order to construct the associated "sparse deconvolved predictive network", i.e., a graph with the same nodes of those compared but whose edge weights are defined by their relevance for out of sample predictions in classification. We present an application of the proposed method by decoding the covert attention direction (left or right) based on the single-trial functional connectivity matrix extracted from high-frequency magnetoencephalography (MEG) data. Our results demonstrate how network deconvolution matched with sparse classification methods outperforms typical approaches for MEG decoding

Time-Frequency Filter Bank: A Simple Approach for Audio and Music Separation

© 2017 IEEE. Blind Source Separation techniques are widely used in the field of wireless communication for a very long time to extract signals of interest from a set of multiple signals without training data. In this paper, we investigate the problem of separation of the human voice from a mixture of human voice and sounds from different musical instruments. The human voice may be a singing voice in a song or may be a part of some news, broadcast by a channel with background music. This paper proposes a generalized Short Time Fourier Transform (STFT)-based technique, combined with filter bank to extract vocals from background music. The main purpose is to design a filter bank and to eliminate background aliasing errors with best reconstruction conditions, having approximated scaling factors. Stereo signals in time-frequency domain are used in experiments. The input stereo signals are processed in the form of frames and passed through the proposed STFT-based technique. The output of the STFT-based technique is passed through the filter bank to minimize the background aliasing errors. For reconstruction, first an inverse STFT is applied and then the signals are reconstructed by the OverLap-Add method to get the final output, containing vocals only. The experiments show that the proposed approach performs better than the other state-of-the-art approaches, in terms of Signal-to-Interference Ratio (SIR) and Signal-to-Distortion Ratio (SDR), respectively

FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels

Temporal point processes (TPP) are a natural tool for modeling event-based data. Among all TPP models, Hawkes processes have proven to be the most widely used, mainly due to their simplicity and computational ease when considering exponential or non-parametric kernels. Although non-parametric kernels are an option, such models require large datasets. While exponential kernels are more data efficient and relevant for certain applications where events immediately trigger more events, they are ill-suited for applications where latencies need to be estimated, such as in neuroscience. This work aims to offer an efficient solution to TPP inference using general parametric kernels with finite support. The developed solution consists of a fast L2 gradient-based solver leveraging a discretized version of the events. After supporting the use of discretization theoretically, the statistical and computational efficiency of the novel approach is demonstrated through various numerical experiments. Finally, the effectiveness of the method is evaluated by modeling the occurrence of stimuli-induced patterns from brain signals recorded with magnetoencephalography (MEG). Given the use of general parametric kernels, results show that the proposed approach leads to a more plausible estimation of pattern latency compared to the state-of-the-art

Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed

Temporal dynamics of MEG phase information during speech perception: Segmentation and neural communication using mutual information and phase locking

The incoming speech stream contains a rich amount of temporal information. In particular, information on slow time scales, the delta and theta band (125 - 1000 ms, 1 - 8 Hz), corresponds to prosodic and syllabic information while information on faster time scales (20-40 ms, 25 - 50 Hz) corresponds to feature/phonemic information. In order for speech perception to occur, this signal must be segregated into meaningful units of analysis and then processed in a distributed network of brain regions. Recent evidence suggests that low frequency phase information in the delta and theta bands of the Magnetoencephalography (MEG) signal plays an important role for tracking and segmenting the incoming signal into units of analysis. This thesis utilized a novel method of analysis, Mutual Information (MI) to characterize the relative information contributions of these low frequency phases. Reliable information pertaining to the stimulus was present in both delta and theta bands (3 - 5 Hz, 5 - 7 Hz) and information within each of these three sub-bands was independent of each other. A second experiment demonstrated that the information present in these bands differed significantly for speech and a non-speech control condition, suggesting that contrary to previous results, a purely acoustic hypothesis of this segmentation is not supported. A third experiment found that both low (delta and theta) and high (gamma) frequency information is utilized to facilitate communication between brain areas thought to underlie speech perception. Distinct auditory/speech networks that operated exclusively using these frequencies were revealed, suggesting a privileged role for these timescales for neural communication between brain regions. Taken together these results suggest that timescales that correspond linguistically to important aspects of the speech stream also facilitate segmentation of the incoming signal and communication between brain areas that perform neural computation

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

FGPA Implementation of Low-Complexity ICA Based Blind Multiple-Input-Multiple-Output OFDM Receivers

In this thesis Independent Component Analysis (ICA) based methods are used for blind detection in MIMO systems. ICA relies on higher order statistics (HOS) to recover the transmitted streams from the received mixture. Blind separation of the mixture is achieved based on the assumption of mutual statistical independence of the source streams. The use of HOS makes ICA methods less sensitive to Gaussian noise. ICA increase the spectral efficiency compared to conventional systems, without any training/pilot data required. ICA is usually used for blind source separation (BSS) from their mixtures by measuring non-Gaussianity using Kurtosis. Many scientific problems require FP arithmetic with high precision in their calculations. Moreover a large dynamic range of numbers is necessary for signal processing. FP arithmetic has the ability to automatically scale numbers and allows numbers to be represented in a wider range than fixed-point arithmetic. Nevertheless, FP algorithm is difficult to implement on the FPGA, because the algorithm is so complex that the area (logic elements) of FPGA leads to excessive consumption when implemented. A simplified 32-bit FP implementation includes adder, Subtractor, multiplier, divider, and square rooter The FPGA design is based on a hierarchical concept, and the experimental results of the design are presented

Study of an attitude reference system utilizing an electrically suspended gyro final report, 1 aug. 1964 - 31 mar. 1965

Miniature electrically suspended gyroscope for spacecraft attitude reference syste

Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI

There are significant milestones in modern human's civilization in which mankind stepped into a different level of life with a new spectrum of possibilities and comfort. From fire-lighting technology and wheeled wagons to writing, electricity and the Internet, each one changed our lives dramatically. In this paper, we take a deep look into the invasive Brain Machine Interface (BMI), an ambitious and cutting-edge technology which has the potential to be another important milestone in human civilization. Not only beneficial for patients with severe medical conditions, the invasive BMI technology can significantly impact different technologies and almost every aspect of human's life. We review the biological and engineering concepts that underpin the implementation of BMI applications. There are various essential techniques that are necessary for making invasive BMI applications a reality. We review these through providing an analysis of (i) possible applications of invasive BMI technology, (ii) the methods and devices for detecting and decoding brain signals, as well as (iii) possible options for stimulating signals into human's brain. Finally, we discuss the challenges and opportunities of invasive BMI for further development in the area.Comment: 51 pages, 14 figures, review articl